import numpy as np
from statsmodels.tools.tools import Bunch


mpg_bs = Bunch()

mpg_bs.smooth0 = Bunch()
mpg_bs.smooth0.term = 'weight'
mpg_bs.smooth0.bs_dim = 12
mpg_bs.smooth0.dim = 1
mpg_bs.smooth0.p_order = np.array([
    3, 2
    ])
mpg_bs.smooth0.by = 'NA'
mpg_bs.smooth0.label = 's(weight)'
mpg_bs.smooth0.sp = -1
mpg_bs.smooth0.m = np.array([
    3, 2
    ])
mpg_bs.smooth0.knots = np.array([
    1488, 1488, 1488, 1488, 1953.22222222, 2118.77777778, 2275,
    2383.88888889, 2515.55555556, 2757.33333333, 3016.44444444,
    3208.11111111, 4066, 4066, 4066, 4066
    ])
mpg_bs.smooth0.rank = 10
mpg_bs.smooth0.null_space_dim = 1
mpg_bs.smooth0.df = 11
mpg_bs.smooth0.S_scale = 2.44395544177397e-06
mpg_bs.smooth0.vn = 'weight'
mpg_bs.smooth0.first_para = 5
mpg_bs.smooth0.last_para = 15
mpg_bs.smooth0.S = np.array([
    0.130569121544375, -0.0191732921244136, 0.0202548028417763,
    0.0393112950002946, 0.032061392663319, 0.0241651485426007,
    0.0238771778381105, 0.0389406835250852, 0.0171569521441248,
    0.00680570834700402, 0.00541920498010491, -0.0191732921244136,
    0.0631473273216884, -0.0592997810399249, 0.0178641542128365,
    0.00461438941034586, 0.00793912465273829, 0.00408821971418319,
    -0.000681869049168392, 0.00213834646721613, 0.000935846814259467,
    0.000163463753965317, 0.0202548028417763, -0.0592997810399249,
    0.201022514824508, -0.186241322892132, 0.00284878067469136,
    0.0287314367063779, 0.00263260805442764, -0.00708846456492193,
    0.00065385484055723, 0.000395067932117418, -0.000586348849229599,
    0.0393112950002947, 0.0178641542128365, -0.186241322892132,
    0.479322071007576, -0.286380917395105, 0.0563704638942206,
    0.0348887744557148, 0.0255203052273876, 0.0138979985789615,
    0.00560832050247454, 0.00383270204450356, 0.032061392663319,
    0.00461438941034587, 0.00284878067469137, -0.286380917395105,
    0.414581586658592, -0.181289913269406, -0.00412646444222532,
    -0.00354719180127285, -0.00206283566879658, -0.000685858743449883,
    -0.00142523589453221, 0.0241651485426007, 0.00793912465273829,
    0.0287314367063779, 0.0563704638942206, -0.181289913269406,
    0.213321319885371, -0.0483506678191679, 0.0110304252072989,
    0.0122299348640366, 0.00357726593043956, 0.00234383478372281,
    0.0238771778381105, 0.00408821971418319, 0.00263260805442764,
    0.0348887744557148, -0.00412646444222533, -0.0483506678191679,
    0.0999220996092033, -0.0356092452156359, 0.00921547832469341,
    0.00389860712399989, 0.00147745030481206, 0.0389406835250852,
    -0.000681869049168392, -0.00708846456492192, 0.0255203052273876,
    -0.00354719180127286, 0.0110304252072989, -0.0356092452156359,
    0.0574427000604467, -0.0102653399490586, -0.000311188772793241,
    0.00285258581655637, 0.0171569521441248, 0.00213834646721613,
    0.000653854840557233, 0.0138979985789615, -0.00206283566879658,
    0.0122299348640366, 0.00921547832469341, -0.0102653399490586,
    0.0189789273643945, -0.0062453384832703, 0.00255839876648785,
    0.00680570834700402, 0.000935846814259467, 0.000395067932117419,
    0.00560832050247454, -0.000685858743449884, 0.00357726593043956,
    0.00389860712399989, -0.000311188772793241, -0.0062453384832703,
    0.0211496391608495, -0.0111654555301857, 0.00541920498010491,
    0.000163463753965317, -0.000586348849229599, 0.00383270204450356,
    -0.00142523589453221, 0.00234383478372281, 0.00147745030481206,
    0.00285258581655637, 0.00255839876648785, -0.0111654555301857,
    0.00794139033444708
    ]).reshape(11, 11, order='F')

mpg_bs.coefficients = np.array([
    29.6272774569595, -6.21365498504518, 1.43986598470837, 1.01128095138012,
    20.1053719083286, -1.26007449980292, -5.26871981200625,
    -4.544036357677, -7.60063071956733, -5.01036711884368,
    -6.96226144900638, -9.06722069409647, -8.81829781369916,
    -7.9145836553663, -6.28068941724657, 5.09307848346347,
    1.90848821039499, -0.646225516186639, -1.50240395085899,
    -4.19244286007642, -5.72993924243941, -6.83323296859843,
    -5.77088950575513, -4.29112523442438
    ])
mpg_bs.fitted_values = np.array([
    21.9396113442978, 21.9396113442978, 18.7594768791121, 23.978775547494,
    19.6605284085114, 22.5415570425591, 21.4364029092864, 21.5001397125411,
    19.345544773382, 17.3284985371825, 23.0829354555269, 23.0829354555269,
    20.5257074462847, 20.350418275392, 20.0920273672739, 16.3475493433762,
    15.2683918200041, 14.6016645854518, 48.1414677548395, 34.6770647570973,
    33.3220963193152, 34.9078179723597, 34.9078179723597, 25.7413956426768,
    31.8998747894453, 31.4406608644666, 31.4406608644666, 25.4076816004152,
    24.637322541799, 19.5423694752458, 43.7120662014648, 36.1794319009933,
    37.8600814894106, 31.3973345386248, 30.9527603762328, 29.8740452948944,
    29.6825700516209, 26.8853631419929, 26.3600803699054, 26.1773024038522,
    25.3856227340742, 23.3020193188726, 24.7100852851954, 26.4172597325081,
    34.6770647570973, 33.3220963193152, 23.0296498930938, 14.7476201253168,
    14.7476201253168, 14.0557212409125, 34.34782143196, 33.965299405798,
    33.7800699350403, 32.4689563918131, 32.329358068477, 23.1906404151376,
    23.1906404151376, 23.1512875039068, 20.6941238423555, 25.5363151316018,
    25.3886549365523, 25.5363151316018, 25.3886549365523, 34.4607486893619,
    25.3326646650999, 20.7768158749875, 31.8441811103844, 23.0217104934054,
    22.4301358955383, 23.1106217353394, 22.4200843547568, 14.7491664060278,
    14.8191667947128, 14.012803756677, 13.9581303982076, 18.1760880728356,
    33.3190101477328, 32.4975851853098, 31.1829311245072, 25.6555703896226,
    22.6046524037776, 25.6230700315092, 19.5479517982215, 19.6165588348237,
    19.6186479966108, 25.1994604891903, 24.8987688972877, 22.3628638356355,
    22.3628638356355, 34.2300125601631, 39.2885271153262, 33.1621106074804,
    32.5172901213345, 30.742747639841, 32.1452590079292, 30.6934785442198,
    31.6517339944659, 30.5907093672281, 30.9641455088909, 24.6266167464042,
    24.9022880874316, 18.78153275305, 17.1521355867311, 19.0141306741668,
    18.231718904575, 16.8216593127165, 17.7202420891745, 22.0561680452922,
    27.2153336488053, 20.5096211586017, 25.567299743712, 21.9831410020533,
    26.7629956266796, 20.2961667790902, 25.2853678595518, 21.7669720922915,
    26.7629956266796, 18.4910047689371, 33.3190101477328, 25.7413956426768,
    31.8998747894453, 31.4406608644666, 29.7789375739066, 24.637322541799,
    19.1142432042937, 19.2193336362723, 17.6365280620964, 17.6365280620964,
    17.579806089837, 15.8168257322192, 21.8960566915, 21.7074289198647,
    21.6525958338155, 21.4785952775663, 18.9756522798894, 18.9897722666541,
    30.4602645168316, 29.3805961723331, 27.2238321670602, 27.9694092954432,
    27.7274373142384, 24.7585135497211, 24.3629123288499, 21.0288767895242,
    26.8745904429808, 24.038604652806, 24.1756501080763, 20.4312780252146,
    32.4709598995115, 31.6700176166953, 31.9735144592214, 30.0023513412019,
    28.4464554097632, 25.3419682258173, 30.061904278659, 29.8978667285749,
    37.2387365989938, 37.2387365989938, 29.9813024611395, 29.8303048561215,
    29.7392162621016, 29.1611058213974, 28.9573381148783, 23.6489155519235,
    23.2515846177326, 21.6472193877583, 21.6596390100383, 21.6094248571684,
    20.9701064457552, 20.8066296061343, 20.6663348110776, 25.161054576378,
    33.0089371917933, 24.3712852430277, 24.3712852430277, 24.2659916432858,
    18.5704777306123, 18.4763039586984, 17.9248313386425, 17.7576418795043,
    38.0290768582748, 27.2115396047694, 37.9994882599177, 27.1914558802737,
    26.6487573286281, 34.7866136817861, 24.6246815506878, 26.2295193582235,
    26.4988154777346, 21.8800181352396, 33.5183981701922, 24.5373651901876,
    20.8017272859603, 20.6222748754003, 20.8111189208126, 20.5838532565161,
    18.3170655814092, 17.5023519244001, 20.8093138226564, 18.3617172827568,
    19.5919656568361, 26.0100107405061, 20.4732985733418
    ])
mpg_bs.linear_predictors = mpg_bs.fitted_values
mpg_bs.deviance = 871.169775911354
mpg_bs.null_deviance = 8721.1724137931
mpg_bs.iter = 1
mpg_bs.weights = np.array([
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
    ])
mpg_bs.prior_weights = np.array([
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
    ])
mpg_bs.df_null = 202
mpg_bs.y = np.array([
    21, 21, 19, 24, 18, 19, 19, 19, 17, 16, 23, 23, 21, 21, 20, 16, 16, 15,
    47, 38, 38, 37, 31, 24, 31, 31, 31, 24, 24, 19, 49, 31, 38, 30, 30, 30,
    30, 27, 27, 27, 27, 24, 25, 24, 38, 38, 24, 15, 15, 13, 30, 31, 31, 31,
    31, 17, 17, 17, 16, 26, 26, 26, 26, 36, 26, 19, 31, 22, 22, 22, 22, 16,
    16, 14, 14, 19, 37, 31, 31, 24, 23, 25, 19, 19, 19, 25, 25, 23, 23, 31,
    45, 31, 31, 31, 31, 31, 31, 31, 31, 27, 27, 17, 17, 19, 19, 17, 19, 19,
    28, 19, 25, 19, 28, 19, 25, 19, 28, 18, 37, 24, 31, 31, 31, 24, 19, 19,
    17, 17, 17, 17, 21, 21, 21, 21, 19, 19, 31, 26, 26, 32, 28, 26, 24, 24,
    28, 25, 23, 23, 35, 31, 31, 31, 27, 27, 30, 30, 34, 38, 38, 28, 28, 29,
    29, 26, 26, 24, 24, 24, 24, 24, 24, 29, 30, 27, 27, 27, 20, 19, 20, 19,
    37, 27, 37, 27, 27, 37, 26, 24, 24, 19, 33, 25, 23, 23, 24, 24, 17, 17,
    23, 19, 18, 26, 19
    ])
mpg_bs.residuals = mpg_bs.y - mpg_bs.fitted_values
mpg_bs.sig2 = 4.70648213227865
mpg_bs.edf_all = np.array([
    0.999999999999898, 1.00000000000005, 1.00000000000003, 1.00000000000002,
    0.715182726327133, 0.945523457125076, 0.933952133938362,
    0.910787981046657, 0.918439669310707, 0.936922915439925,
    0.967620005020078, 0.978502846618072, 0.979593620228742,
    0.973114724412765, 0.993928896515985, 0.206529848573726,
    0.134722764346217, 0.130032558435079, 0.214058208629181,
    0.428628830751172, 0.480753053928721, 0.728730191076721,
    0.464984912297227, 0.858004843431828
    ])
mpg_bs.edf1 = np.array([
    0.999999999999765, 1.00000000000012, 1.00000000000008, 1.00000000000005,
    0.895328939039617, 0.985281018312391, 0.980485303917254,
    0.985115307594987, 0.979973329752608, 0.991029113855046,
    0.996293301947028, 0.996456521529546, 0.997881056094132,
    0.998369079216076, 0.999638164901435, 0.263857404503542,
    0.167489975859197, 0.241183045516588, 0.33714799512247,
    0.51312433111897, 0.537892914498989, 0.811390270624644,
    0.600430783273412, 0.922162336898961
    ])
mpg_bs.hat = np.array([
    0.078074509536494, 0.078074509536494, 0.065030323239247,
    0.0401345230539626, 0.168187944335173, 0.0664399958506056,
    0.0736573430018038, 0.0780134136835909, 0.070154483540544,
    0.180767093886445, 0.0664593954539867, 0.0664593954539867,
    0.0594553974001933, 0.0675038468082751, 0.0536944450768444,
    0.0970468595627894, 0.125358300385429, 0.125631704260218,
    0.944302622125637, 0.0519142263263012, 0.0379831848669348,
    0.0493844279965059, 0.0493844279965059, 0.0949083347321329,
    0.0309819618026538, 0.029589102491272, 0.029589102491272,
    0.0670447385890157, 0.0842123937662785, 0.0687733897932081,
    0.422927507867707, 0.1328199698695, 0.096736475340286,
    0.0419915413707043, 0.0396238084435642, 0.0346918436236485,
    0.0363065585955308, 0.0491064279890273, 0.0441863897171056,
    0.0388360718854439, 0.0414457600258062, 0.0451305758954266,
    0.0505018876352514, 0.0744589274009074, 0.0519142263263012,
    0.0379831848669348, 0.101629248471346, 0.436525380451652,
    0.436525380451652, 0.410550032148353, 0.0425422548236529,
    0.039274955616929, 0.0380389889031733, 0.0330421266803994,
    0.0325988284653424, 0.066244431441902, 0.066244431441902,
    0.0666221560337819, 0.0952529331187707, 0.046598430925228,
    0.0484713995290722, 0.046598430925228, 0.0484713995290722,
    0.11544865825422, 0.0480259855055657, 0.0566600271329405,
    0.137824712103277, 0.112620492286817, 0.182195648532627,
    0.11200306230358, 0.188335501134619, 0.169514959934583,
    0.158055134563875, 0.160687809078496, 0.168998857825895,
    0.0681007619372312, 0.0357408884807467, 0.0331290501952606,
    0.0300353569162307, 0.0866855568546318, 0.0663539796978426,
    0.0330200310685004, 0.0646249177791591, 0.0586792000782014,
    0.0592011499253728, 0.0382030583658832, 0.0445521201486119,
    0.067008407725231, 0.067008407725231, 0.0432898262619123,
    0.12461566211325, 0.0358742808101827, 0.03362714942096,
    0.0325446423810936, 0.0323846714084075, 0.0335509666038901,
    0.0303353227561161, 0.0362607404205336, 0.0298241376272862,
    0.0362837776795306, 0.0432688893651977, 0.0709252762463711,
    0.147397446518277, 0.0723900341977973, 0.0517894283747262,
    0.106665126964701, 0.0503219409131233, 0.0834357951937577,
    0.100818501015755, 0.105292582127573, 0.104194456647628,
    0.0762380011365815, 0.105895524421508, 0.131464943163143,
    0.105727045973233, 0.0727635750299144, 0.105895524421508,
    0.0437337708748914, 0.0357408884807467, 0.0949083347321329,
    0.0309819618026538, 0.029589102491272, 0.0529778772840967,
    0.0842123937662785, 0.0607153181824941, 0.0672240685836559,
    0.144807911379215, 0.144807911379215, 0.141129878619691,
    0.688420146600013, 0.0609351365077056, 0.0626297831888101,
    0.0648027622574709, 0.0759997034218865, 0.0782103847541935,
    0.0707477713155958, 0.0410921970518355, 0.0589697594313821,
    0.152256577677227, 0.056765876958851, 0.0440206300688631,
    0.0331810206918762, 0.145634415508243, 0.171438771769591,
    0.045261532361592, 0.041840944187868, 0.143150260376584,
    0.156242430028033, 0.0410741399980494, 0.0522187899853102,
    0.0442706334210004, 0.0971923907649902, 0.165585071470526,
    0.238054218005531, 0.0532738449881275, 0.0604428830039113,
    0.111392476904907, 0.111392476904907, 0.057580243342699,
    0.0605291866378679, 0.0573770911039481, 0.0885019494515106,
    0.0862447970676653, 0.0955397769716847, 0.081739783220223,
    0.0812965346829218, 0.0809796720150843, 0.0813844390983633,
    0.0553602073413133, 0.0595903222287986, 0.0580738296478883,
    0.0331472062547294, 0.117186329269605, 0.0421565210307805,
    0.0421565210307805, 0.0431360897292105, 0.0601728243619919,
    0.0608147839961041, 0.0469900387599093, 0.0511099791683115,
    0.133968859142191, 0.0454828817219972, 0.134269192729732,
    0.045677409250904, 0.0484924110945302, 0.0915494575563987,
    0.0473953480872363, 0.0521826902889385, 0.0499074879741539,
    0.0607510996298348, 0.138895050565221, 0.0906618240478993,
    0.0545651794730959, 0.0588322152774606, 0.0555824719807064,
    0.0578268424764083, 0.0579806773332134, 0.0555747554696179,
    0.0570504628526928, 0.0556475846732603, 0.0510369790482531,
    0.105422478219541, 0.0549738873644795
    ])
mpg_bs.R = np.array([
    -14.247806848775, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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mpg_bs.sp = np.array([
    0.830689464223685, 425.361212061649
    ])
mpg_bs.nsdf = 4
mpg_bs.Ve = np.array([
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    0.240131184980999, 0.4134406891463, -0.575046038411989,
    4.04150194703527, 0.746794237585425, 0.448761523202594,
    -0.36676854858092, -0.105116727222253, -0.152318039573587,
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    -0.321958833095722, -0.5732539695505, -0.343184385441028,
    -0.12956213066101, 0.0326656898702012, 0.0898255256880958,
    0.241496481383994, 0.339886403782703, 0.249267438526421,
    0.746794237585425, 3.59868564812178
    ]).reshape(24, 24, order='F')

mpg_bs.rV = np.array([
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    -0.000309898734480696, -5.42025622874735e-06, -2.22599252448511e-05,
    6.60370601160911e-05, 4.41786063148996e-05, 0.000264079369629404,
    0.000312739927356773, 0.000166746800984281, 6.22512704032769e-05,
    0.00023873784102746, -0.000189663494086306, -0.000244118781964904,
    -5.82504534029272e-05, 0.000142876370789319, 0.000217254008213435,
    4.53282887363646e-05, 1.82517606339378e-05, 8.58792421331435e-06,
    0.000370322487453743, 0.000201992910699855, -0.000469164613014575,
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    0.000120435765907176, 0.000158193260135929, 0.000318437252371348,
    0.000133569503239669, 5.14508949733296e-05, 3.72708649769547e-05,
    0.0481141951754968, -0.0112467428647124, 0.0133577786074344,
    0.00971188595873735, 0.0144420339639255, 0.0153033177968391,
    0.006359918898083, 0.00184703219892869, 0.000682133802387619,
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    -0.00300932642921946, 0.000227034785457444, 0.00126423894576831,
    0.00172845539987259, -1.90970302574768e-05, -0.000518679088367633,
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    -0.0676047713589873, -0.0647127382787066, -0.0818438736871805,
    -0.0939009139216642, -0.0902348993636832, -0.0508630584433755,
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    0.0663163083142627, 0.0640123302445087, 0.0750880869238639
    ]).reshape(24, 24, order='F')

mpg_bs.gcv_ubre = 5.16162045425616
mpg_bs.aic = 909.585145453506
mpg_bs.rank = 24
mpg_bs.gcv_ubre_dev = 5.16162045425363
mpg_bs.method = 'GCV'
mpg_bs.cmX = np.array([
    1, 0.901477832512315, 0.58128078817734, 0.374384236453202, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    ])
mpg_bs.assign = np.array([
    0, 1, 2, 2
    ])
mpg_bs.offset = np.array([
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    ])
mpg_bs.df_residual = 185.099985812547
mpg_bs.min_edf = 6
mpg_bs.optimizer = 'magic'
mpg_bs.p_coeff = np.array([
    29.6272774569595, -6.21365498504518, 1.43986598470837, 1.01128095138012
    ])
mpg_bs.se = np.array([
    1.10736522428059, 0.730964352033962, 0.802742257532199,
    0.82715120287436, 3.45934930908735, 1.72348162068993, 1.34626504738022,
    1.128648591521, 1.23820355203725, 1.25079638223793, 1.32555503230954,
    1.46276521490991, 2.08682079164069, 2.44820576733618, 1.57664310132803,
    0.706341710395395, 0.340307783146933, 0.439430720698385,
    0.475893350342798, 0.681557125789893, 0.862789472579143,
    1.29715423972101, 2.01034871279469, 1.89702020234941
    ])
mpg_bs.p_t = np.array([
    26.7547479434413, -8.50062655963349, 1.7936840513851, 1.22260712172806
    ])
mpg_bs.p_pv = np.array([
    1.61958620027156e-65, 6.17307801841172e-15, 0.0744959485685591,
    0.223032563883354
    ])
mpg_bs.residual_df = 185.099985812547
mpg_bs.m = 2
mpg_bs.chi_sq = np.array([
    132.00007563458, 69.3539925351537
    ])
mpg_bs.s_pv = np.array([
    4.72304400617769e-18, 6.21718617954453e-12
    ])
mpg_bs.scale = 4.70648213227865
mpg_bs.r_sq = 0.890988350463498
mpg_bs.n = 203
mpg_bs.dev_expl = 0.900108639690056
mpg_bs.edf = np.array([
    10.2535689759835, 3.64644521146987
    ])
mpg_bs.dispersion = 4.70648213227865
mpg_bs.pTerms_pv = np.array([
    6.1730780184117e-15, 0.175815884801339
    ])
mpg_bs.pTerms_chi_sq = np.array([
    72.2606519063463, 3.50949117352962
    ])
mpg_bs.pTerms_df = np.array([
    1, 2
    ])
mpg_bs.cov_unscaled = np.array([
    0.260546562269068, -0.127261644910421, -0.147454452733496,
    -0.147400601308703, 0.0858934363413853, 0.0324027409627562,
    0.001951459420489, -0.0444711495173745, -0.051050382403525,
    -0.0558058242079355, -0.0776843093730959, -0.0696248432860819,
    -0.143873948165499, -0.102565827833482, -0.0504628003799288,
    -0.0838215012227384, -0.0264331690405809, 0.0189063712819891,
    0.0354335965549041, 0.0710226222907629, 0.086637653107266,
    0.101618191765279, 0.081840238740399, 0.0953496710685112,
    -0.127261644910421, 0.11352616857503, 0.025864159966478,
    0.026405976204483, -0.0647092342623571, -0.0194747421679251,
    0.0013822670878288, 0.0319763850603683, 0.034430423334006,
    0.0345203757451707, 0.0608821466756475, 0.0407664667130339,
    0.130282891645105, 0.0851176416918229, 0.0400522748720902,
    0.0629753993433594, 0.0181263443541013, -0.0160778765682171,
    -0.0267074236912963, -0.0523474645302372, -0.0639429975967239,
    -0.0786117565174105, -0.063904170832196, -0.077928384358563,
    -0.147454452733496, 0.025864159966478, 0.136916515120372,
    0.118999524300398, -0.0327647151474401, -0.0198952632533516,
    -0.00672761172219511, 0.015354304214709, 0.0188841458743991,
    0.0289212388819706, 0.0254660969027705, 0.0406957842836357,
    0.0400286710723789, 0.0342842902886422, 0.0236887528118937,
    0.0259618144092581, 0.00960697675201729, -0.00467922337824681,
    -0.0118300130244149, -0.022700519316666, -0.0264358122220455,
    -0.0335761909581523, -0.0153555815191469, -0.022334457938622,
    -0.147400601308703, 0.026405976204483, 0.118999524300398,
    0.145369533589466, -0.0227413331564733, -0.0087662833528124,
    0.00190469794456696, 0.0179494340611051, 0.0241333019028084,
    0.0210348337470021, 0.0213616121916855, 0.0246247004357953,
    0.00843762000002028, 0.0157735311385979, 0.00156722820696845,
    0.0319443758959039, 0.0120419610749916, -0.00452096841614408,
    -0.011968684582334, -0.028412434692433, -0.0364006470838577,
    -0.0300072493763622, -0.0408835076677611, -0.0323634585859656,
    0.0858934363413853, -0.0647092342623571, -0.0327647151474401,
    -0.0227413331564733, 2.54268417598119, -0.76859110341217,
    0.285406451366382, -0.104994244590691, 0.0177218270496262,
    -0.0481109774851848, -0.0409982342644028, -0.0444588957252262,
    -0.0806376771582389, -0.0827972314624984, -0.0490348449178703,
    -0.0673431972324915, -0.0168710490878408, 0.0189552170264795,
    0.0286066089048379, 0.0551838149903664, 0.0723392929084603,
    0.0763983959054345, 0.0512365416163641, 0.0602549766706101,
    0.0324027409627562, -0.0194747421679251, -0.0198952632533516,
    -0.0087662833528124, -0.76859110341217, 0.631127201457765,
    -0.111958277080447, 0.187556063652864, 0.066179499479836,
    0.0804163944044619, 0.0298368025897733, 0.0970443447402095,
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    -0.0273469708991807, -0.0264787364275103, -0.0125691792814477,
    0.00392141065820518, 0.0241964117675904, 0.0349487881953121,
    0.0398279043446865, 0.0271145839091361, 0.0312444446552389,
    0.001951459420489, 0.0013822670878288, -0.00672761172219511,
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    0.0971768028496673, 0.127578101317701, 0.194137807287134,
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    0.0684932186535236, 0.270658127947807, 0.0927495887348496,
    0.148746573370328, 0.0894230227281297, 0.178358167405521,
    0.0971648415678839, 0.0482289443030786, 0.0209345589972529,
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    0.0241333019028084, 0.0177218270496262, 0.066179499479836,
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    0.0540054156093696, 0.196470598735639, 0.21878824740985,
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    0.0971768028496673, 0.148746573370328, 0.0540054156093696,
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    0.0359008074045836, 0.111069102387542, 0.0147778467074979,
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    0.0213616121916855, -0.0409982342644028, 0.0298368025897733,
    0.127578101317701, 0.0894230227281297, 0.196470598735639,
    -0.0207364863609935, 0.373335347781393, 0.1104631596741,
    0.284840959571074, 0.0105060767846668, 0.102975779715763,
    0.0867862639453907, 0.0243900586439621, -0.0203947322272078,
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    -0.0696248432860819, 0.0407664667130339, 0.0406957842836357,
    0.0246247004357953, -0.0444588957252262, 0.0970443447402095,
    0.194137807287134, 0.178358167405521, 0.21878824740985,
    0.207339580194955, 0.1104631596741, 0.454624497408751,
    -0.0714258011738595, 0.310716907708582, 0.0601826022359559,
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    0.0495142478924261, 0.0867862639453907, 0.089018060168629,
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    0.0120419610749916, -0.0168710490878408, -0.0264787364275103,
    -0.00896910696738203, 0.00160852784573758, 0.00795472458798096,
    0.00840110351440287, 0.0243900586439621, 0.0213745513074023,
    0.0217225069458946, 0.0332438408000797, 0.0206050701500866,
    0.0215080027514771, 0.0246063586380409, 0.00847331422875254,
    -0.00270662509257549, -0.0166664535611419, -0.0268399881068754,
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    0.0189063712819891, -0.0160778765682171, -0.00467922337824681,
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    -0.028771799405471, -0.0193956772787635, -0.0375872201518705,
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    -0.037843257632743, 0.00847331422875254, 0.0410283844422061,
    0.0340956221608027, 0.0371845294194256, 0.0290913575478051,
    0.0143241042112522, 0.00594206245237508, 0.00694057449961806,
    0.0354335965549041, -0.0267074236912963, -0.0118300130244149,
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    0.0481196941867164, 0.0512995083369737, 0.0427880770540714,
    0.025938901829721, 0.0172406430026829, 0.0190854917034619,
    0.0710226222907629, -0.0523474645302372, -0.022700519316666,
    -0.028412434692433, 0.0551838149903664, 0.0241964117675904,
    -0.0297690048221073, -0.033120623649721, -0.0711759115486841,
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    0.0512995083369737, 0.0986979452294408, 0.0917669226619505,
    0.0635525514325997, 0.0510213739757127, 0.0513114624886664,
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    0.0427880770540714, 0.0917669226619505, 0.158166046969139,
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    0.025938901829721, 0.0635525514325997, 0.0526478012750861,
    0.357508872728168, -0.122181710723627, 0.0529625804413154,
    0.081840238740399, -0.063904170832196, -0.0153555815191469,
    -0.0408835076677611, 0.0512365416163641, 0.0271145839091361,
    -0.00499252275484576, -0.015226518459989, -0.0374137141732135,
    -0.00831843730650562, -0.111064756608917, -0.0196286635230926,
    -0.105907217560377, -0.0757024271527446, -0.144246293596641,
    -0.0721417252895369, -0.0272696852211407, 0.00594206245237508,
    0.0172406430026829, 0.0510213739757127, 0.0878449503315404,
    -0.122181710723627, 0.858709718521459, 0.158673552049344,
    0.0953496710685112, -0.077928384358563, -0.022334457938622,
    -0.0323634585859656, 0.0602549766706101, 0.0312444446552389,
    -0.000842958258748316, -0.0170673905077609, -0.03621185498621,
    -0.0139208013344843, -0.0921425296807421, -0.0172844920058013,
    -0.198551268786848, -0.0684075332800308, -0.121800944620384,
    -0.0729173883583565, -0.0275284441796622, 0.00694057449961806,
    0.0190854917034619, 0.0513114624886664, 0.072216656566408,
    0.0529625804413154, 0.158673552049344, 0.764623246615722
    ]).reshape(24, 24, order='F')

mpg_bs.cov_scaled = mpg_bs.cov_unscaled * mpg_bs.scale

mpg_bs.p_table = np.array([
    29.6272774569595, -6.21365498504518, 1.43986598470837, 1.01128095138012,
    1.10736522428059, 0.730964352033962, 0.802742257532199,
    0.82715120287436, 26.7547479434413, -8.50062655963349, 1.7936840513851,
    1.22260712172806, 1.61958620027156e-65, 6.17307801841172e-15,
    0.0744959485685591, 0.223032563883354
    ]).reshape(4, 4, order='F')

mpg_bs.pTerms_table = np.array([
    1, 2, 72.2606519063463, 1.75474558676481, 6.1730780184117e-15,
    0.175815884801339
    ]).reshape(2, 3, order='F')

mpg_bs.s_table = np.array([
    10.2535689759835, 3.64644521146987, 10.8058511361601, 4.39467905741677,
    12.2156111509682, 15.7813555049275, 4.72304400617769e-18,
    6.21718617954453e-12
    ]).reshape(2, 4, order='F')

mpg_bs.method = 'GCV'
mpg_bs.sp_criterion = 5.16162045425616
mpg_bs.rank = 24
mpg_bs.np = 24
mpg_bs.params = np.array([
    29.6272774569595, -6.21365498504518, 1.43986598470837, 1.01128095138012,
    20.1053719083286, -1.26007449980292, -5.26871981200625,
    -4.544036357677, -7.60063071956733, -5.01036711884368,
    -6.96226144900638, -9.06722069409647, -8.81829781369916,
    -7.9145836553663, -6.28068941724657, 5.09307848346347,
    1.90848821039499, -0.646225516186639, -1.50240395085899,
    -4.19244286007642, -5.72993924243941, -6.83323296859843,
    -5.77088950575513, -4.29112523442438
    ])
